add Bouhadjar sequence learning network tutorial

nest · Nov 16, 2024 · a512881 · a512881
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diff --git a/doc/tutorials/sequence_learning/iaf_psc_exp_nonlineardendrite_neuron.nestml b/doc/tutorials/sequence_learning/iaf_psc_exp_nonlineardendrite_neuron.nestml
@@ -30,7 +30,7 @@ model iaf_psc_exp_nonlineardendrite_neuron:
         V_m mV = 0 mV         # membrane potential in mV
         dAP_trace pA = 0 pA             # dAP trace
         active_dendrite boolean = false
-        active_dendrite_readout real = 0.
+        active_dendrite_readout real = 0.    # an extra readout is included so the state can be recorded in NEST; see https://github.com/nest/nestml/issues/986
         dAP_counts integer = 0
         ref_counts integer = 0
         I_dend pA = 0 pA
@@ -41,42 +41,40 @@ model iaf_psc_exp_nonlineardendrite_neuron:
         kernel I_kernel1 = exp(-1/tau_syn1*t)
 
         # alpha shaped postsynaptic current kernel
-        #kernel I_kernel2 = (e/tau_syn2) * t * exp(-t/tau_syn2)
         I_dend' = I_dend$ - I_dend / tau_syn2
         I_dend$' = -I_dend$ / tau_syn2
 
         # exponential shaped postsynaptic current kernel
         kernel I_kernel3 = exp(-1/tau_syn3*t)
 
         # diff. eq. for membrane potential
-        #recordable inline I_dend pA = convolve(I_kernel2, I_2) * pA
         inline I_syn pA = convolve(I_kernel1, I_1) * pA - convolve(I_kernel3, I_3) * pA + I_e
         V_m' = -(V_m - E_L)/tau_m + (I_syn + I_dend) / C_m
 
         # diff. eq. for dAP trace
         dAP_trace' = -evolve_dAP_trace * dAP_trace / tau_h
 
     parameters:
-        C_m pF = 250 pF                    # capacity of the membrane
-        tau_m ms = 20 ms                 # membrane time constant.
+        C_m pF = 250 pF                # capacitance of the membrane
+        tau_m ms = 20 ms               # membrane time constant
         tau_syn1 ms = 10 ms            # time constant of synaptic current, port 1
         tau_syn2 ms = 10 ms            # time constant of synaptic current, port 2
         tau_syn3 ms = 10 ms            # time constant of synaptic current, port 3
-        tau_h ms = 400 ms                # time constant of the dAP trace
-        V_th mV = 25 mV                    # spike threshold
-        V_reset mV = 0 mV                # reset voltage
-        I_e        pA = 0pA                    # external current.
-        E_L        mV = 0mV                    # resting potential.
-        evolve_dAP_trace real = 1        # set to 0 to stop integrating dAP_trace
+        tau_h ms = 400 ms              # time constant of the dAP trace
+        V_th mV = 25 mV                # spike threshold
+        V_reset mV = 0 mV              # reset voltage
+        E_L mV = 0mV                   # resting potential.
+        evolve_dAP_trace real = 1      # set to 0 to stop integrating dAP_trace
+        I_e pA = 0pA                   # external current.
 
         # dendritic action potential
         theta_dAP pA = 60 pA                            # current threshold for a dendritic action potential
-        I_p pA = 250 pA                                     # current clamp value for I_dAP during a dendritic action potential
-        tau_dAP ms = 60 ms                                # time window over which the dendritic current clamp is active
+        I_p pA = 250 pA                                 # current clamp value for I_dAP during a dendritic action potential
+        tau_dAP ms = 60 ms                              # time window over which the dendritic current clamp is active
         dAP_timeout_ticks integer = steps(tau_dAP)
 
         # refractory parameters
-        t_ref ms = 10 ms                                                        # refractory period
+        t_ref ms = 10 ms                                # refractory period
         ref_timeout_ticks integer = steps(t_ref)
 
         I_dend_incr pA/ms = pA * exp(1) / tau_syn2
@@ -91,7 +89,7 @@ model iaf_psc_exp_nonlineardendrite_neuron:
         spike
 
     onReceive(I_2):
-        I_dend$ += I_2 * ms * I_dend_incr * 1E6 # XXX factor 1E6?!
+        I_dend$ += I_2 * s * I_dend_incr
 
     update:
         # solve ODEs
@@ -114,9 +112,9 @@ model iaf_psc_exp_nonlineardendrite_neuron:
                     active_dendrite = false
                     active_dendrite_readout = 0.
 
-                    # the following assignment to I_dend$ reproduces a bug in the original implementation
+                    # the following assignment to I_dend$ reproduces a bug in the original implementation. It is included here to replicate the results of the original model
                     c1 real = -resolution() * exp(-resolution() / tau_syn2) / tau_syn2**2
-                    c2 real = (-resolution() + tau_syn2)*exp(-resolution() / tau_syn2)/tau_syn2
+                    c2 real = (-resolution() + tau_syn2) * exp(-resolution() / tau_syn2) / tau_syn2
                     I_dend$ = I_p * c1 / (1 - c2) / ms
 
             else: